「questions」の検索結果

Prepare Korean interview follow-up questions and evidence-backed answer points from resume.md, JD analysis, cover letter text, and an evidence map. Use after HR review or final cover letter drafting when the user wants 면접 준비, 꼬리질문, interview defense, or claim verification before manual submission.

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Manual testing + bug filing for Enterprise / SaaS products. Parses a Feishu test doc (scope + repo + bitable URL), observes staging via claude-in-chrome, captures DOM/console/network/screenshot evidence, then dual-tracks each finding — Feishu Bitable record (with attachments) + GitHub issue (<REPO>#<ISSUE_NUMBER> format — English-primary, Chinese collapsed, R2-hosted screenshots, matching type label). Enforces dedup across bitable + GitHub issues + PRs, and mandatory P0/P1/P2 priority prompt when unclear. Trigger whenever the user says "test and report", "test and report side-by-side", "start system testing", "experience staging", "collaborate on bug filing", "collect feedback", a shared Feishu testing doc, a bitable URL + finding, or any UX/bug needing both bitable + GitHub tracking. Do NOT use for writing product code, PR review, CI debugging, or general "how do I use this product" questions — those go to normal dev / PR-review / support flows.

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Use when: investigating a legacy, inherited, third-party-built, or otherwise newly-unfamiliar codebase before scoping a migration or major refactor. Formalizes a repeatable five-step investigation pass — entry-point census, business-rule classification, side-effect/idempotency cataloging, implicit-contract discovery, semantic-duplication detection — plus a canonical per-project Open Questions document. Requires checkable signals: ticket keywords or prior documentation gaps. Not a mandatory gate; reserved for entry-point-heavy work in code areas with no prior investigation record.

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Use when: investigating a legacy, inherited, third-party-built, or otherwise newly-unfamiliar codebase before scoping a migration or major refactor. Formalizes a repeatable five-step investigation pass — entry-point census, business-rule classification, side-effect/idempotency cataloging, implicit-contract discovery, semantic-duplication detection — plus a canonical per-project Open Questions document. Requires checkable signals: ticket keywords or prior documentation gaps. Not a mandatory gate;

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Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

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Clarify requirements before implementing. Use when serious doubts arise.

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Ask clarifying questions before implementing to ensure Python requirements are understood. Use when a task lacks detail, dependencies are unclear, or multiple interpretations are possible.

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Use to create, update or verify the Lean 4 executable specification under lean/ from the exploration documents (event-timeline, hotspots, ubiquitous-language, ux-review, model-review) — initial generation, reflecting a session's results, fixing lake build, adding a command or a screen, checking a proposed feature against the model. Formalization questions go to model-review.md as MQ, never to the expert directly.

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Guides and best practices for working with Neon Serverless Postgres. Covers getting started, local development with Neon, choosing a connection method, Neon features, authentication (@neondatabase/auth), PostgREST-style data API (@neondatabase/neon-js), Neon CLI, and Neon's Platform API/SDKs. Use for any Neon-related questions.

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Plan a user interview topic in PostHog — pick who to target (cohort, emails, or PostHog distinct IDs), draft what to ask about, and prepare the voice-agent context plus a question list. Use when the user asks to "talk to users", "check how users feel about X", "interview some customers", "set up a user interview", "run a user-research call", "find users to ask about Y", or otherwise wants qualitative feedback through a conversation. Walks the user through targeting (cohorts-list, persons-list, or accepting emails / distinct IDs directly), captures the topic, and prompts for agent context and questions before calling user-interview-topics-create. Cohort targeting is resolved to explicit emails/distinct_ids at create time — topics snapshot their audience and do not re-evaluate cohort membership later. Do NOT trigger when the user is uploading a recorded interview audio file (that's the separate UserInterview/transcript flow) or only browsing existing topics with user-interview-topics-list.

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Build a throwaway prototype to flesh out a design — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route.

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Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$ai_generation` and `$ai_evaluation` events with `$session_id` linkage to the trigger user's recording (the standard setup post the session-summary linkage PRs).

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Diagnoses bias, anomalies, and strange-looking results on a specific PostHog experiment. Covers empty / 0-exposure experiments, sample ratio mismatch, identity fragmentation, multi-variant exposure, uneven-split exclusion bias, significance traps (peeking, A/A, Bayesian vs Frequentist), PostHog-vs-SQL discrepancies, and surprises after mid-run edits. Symptom-driven dispatch to the right diagnostic. TRIGGER when: user asks 'is my experiment biased?' or 'why 0 exposures?', references the bias banner, says a variant looks strange / wrong / off, sees significance flipping, notices PostHog numbers disagreeing with their SQL, sees an A/A test showing significance, or reports surprises after mid-run edits. DO NOT TRIGGER when: creating a new experiment (use creating-experiments), only configuring rollout (use configuring-experiment-rollout) or metrics (use configuring-experiment-analytics), or only asking lifecycle questions (use managing-experiment-lifecycle).

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GPCR receptor pharmacology — agonist/antagonist/inverse-agonist/biased-agonist classification, GPCRdb structural data, receptor-ligand binding analysis, antibody-target interface (SAbDab). Use for GPCR drug discovery, biased-agonism analysis, receptor subtype selectivity questions, and orthosteric vs allosteric pocket characterization.

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Find commercial sources for chemical compounds — PubChem/ChEMBL identity resolution then vendor catalog search across ZINC, Enamine, eMolecules, Mcule. Compares pricing, availability, and identifies purchasable analogs when an exact compound is not in stock. Use for chemical procurement, virtual library curation, and 'where can I buy X' questions for synthesis planning.

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Completes the online SBTI personality test at sbti.fancc.de5.net, answers all single-choice questions using the model's default personality and stable behavioral tendencies, submits the test, and returns the final type, match percentage, result description, and fifteen-dimension scores. Use when the user asks to take the SBTI test, run the SBTI quiz, get an SBTI result, or complete that personality test website for them.

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Investigate errors, incidents, or product ideas using both internet research and repo-wide codebase research, then propose a concrete changeset plan with clarifying questions before any implementation. Use when a user asks to investigate, root-cause, or plan changes, or when an Issue or PR is referenced and needs a plan.

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Analyzes current state and user query to answer BMad questions or recommend the next skill(s) to use. Use when user asks for help, bmad help, what to do next, or what to start with in BMad.

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ACGME regulatory compliance expertise for medical residency scheduling. Use when validating schedules, checking work hour limits, supervision ratios, or answering compliance questions. Integrates with MCP validation tools.

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Read a Japanese job posting closely and report what it actually states: base pay separated from fixed overtime allowance, the range converted to an hourly basis, required versus preferred requirements, and the terms left ambiguous. Use when a user wants a 求人票 or job description examined before applying, wants two postings compared on the same basis, or wants questions prepared about pay and working conditions; do not use to judge whether the user will be hired, to infer duties the posting does not state, or to evaluate the employer's reputation.

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Check whether a user may start side work alongside their current job, by reading the employer's own rules and contract clause by clause: whether side work is prohibited, needs prior permission, or only a notification, and which duties the planned side work could touch (non-compete, confidentiality, exclusive attention to the main job, use of company assets and information, combined working hours). Separate what is confirmed in a document from what is hearsay, and produce the questions to ask the employer plus the facts an application or notification will need. Use when someone with a main job is considering side work, freelance or gyomu-itaku work, a second part-time job, or paid trial work for another company, and needs to know what their own employer's rules require first; do not use to rule on whether a clause is lawful or enforceable, to help conceal side work from an employer or avoid filing obligations, to predict whether permission will be granted, or to submit the application or contact the employer.

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Reply to an applicant for an employer's Otameshi Tenshoku listing: match the application against the listed requirements item by item on what the applicant actually stated, list the questions still open, and draft an invitation, a request for clarification, a hold notice or a decline that sticks to the listed conditions, keeps other applicants and internal notes out, and presents any new work or dates as a proposal rather than an agreed term. Use when a hiring manager, recruiter or small-business owner has applications to answer, wants to know what to ask before inviting, or has to decline; do not use to judge an applicant's aptitude or predict hiring outcomes, to screen on personal attributes unrelated to the work, to design the work (trial-work-design) or the listing (trial-listing-draft), or to send the message or update applicant records on the user's behalf.

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GitHub Issueの確認事項（最後のコメント・descriptionの両方）を、コードベース・ドキュメント・そこから参照されている外部リンク（仕様書・ライブラリ公式ドキュメント等）まで調査し、根拠に基づいた回答をコメントに追記するスキル。調査しても事実で決まらず人間の意思決定が必要な項目は、固定セクションで明示して後続のトリアージへ引き渡す。

Update an existing GitHub Issue's description based on the issue number. Reads all issue comments and reflects any items not yet captured in the description. After refreshing the description, reviews it for remaining ambiguities or unclear requirements and posts any open questions as a follow-up 確認事項 comment.

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